Monchromate – Smart greyscale browser extension

Hacker News

4 months back I started making an extension named monochromate which was basically a greyscale extension, I realised power of them when I turned it on my phone and the thing is being a programmer most of my time is spent on browsers ... and that's where it don't exist (at least not the perfect solution for my needs). That's how I came up with it, made it open source, recently passed 100 users over chrome webstore and it have 5-star rating as of now. and yes you might say that why would I need it when I can do this with filters that can be toggled via settings, thing is I didn't wanted to greyscale my work sites as well and thus I made site exclusion, also have scheduler, intensity control and above that all it supports all the browsers including Firefox providing same experience. Would love any kind of feedback over this!!

Visit product View on Hacker NewsJul 29, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, started, including · Missing: reddit linkedin, podcasting, created

93%93% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, io · Missing: https docs, excited, just released

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, open · Missing: mac, agents, macos

53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month, users · Missing: mobile apps, ios, personal

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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